Hybrid day-ahead and real-time energy trading of renewable-based multi-microgrids: A stochastic cooperative framework

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-30 DOI:10.1016/j.segan.2024.101516
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Abstract

This paper proposes a multi-objective optimization framework to model the energy trading between microgrids and microgrid communities in the distribution systems. To this end, a hybrid cooperative and non-cooperative algorithm is presented where the microgrid community leads the optimization problem. The microgrid community performs a multi-objective optimization to determine the transactive retail prices to simultaneously improve its operation cost and system flexibility. However, the microgrids, as the followers of the problem, receive the retail prices from the microgrid community to decide on the amount of hourly trading with the microgrid community. The main objective of microgrids is to reduce their cost as much as possible. For this reason, they cooperate to form several coalitions to enhance their bargaining power in the market. Real-time scheduling will be done to increase the reliability of the proposed model and reduce the imbalance costs of the microgrid community and microgrids. The proposed model is tested on a general case study, and the simulation results show that the cooperation among microgrids reduces their operation costs from $ 3453.66 to $ 2984.33. Also, the multi-objective scheduling increases the flexibility by 28.5 %.

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基于可再生能源的多微网的混合日前和实时能源交易:随机合作框架
本文提出了一个多目标优化框架,用于模拟配电系统中微电网和微电网群落之间的能源交易。为此,本文提出了一种合作与非合作混合算法,由微网社区主导优化问题。微电网社区执行多目标优化,确定交易零售价格,以同时改善其运营成本和系统灵活性。然而,微电网作为问题的追随者,从微电网社区接收零售价格,以决定每小时与微电网社区的交易量。微电网的主要目标是尽可能降低成本。因此,微电网通过合作形成多个联盟,以增强其在市场上的议价能力。实时调度将提高拟议模型的可靠性,降低微电网社区和微电网的不平衡成本。仿真结果表明,微电网之间的合作可将运营成本从 3453.66 美元降至 2984.33 美元。此外,多目标调度还将灵活性提高了 28.5%。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
期刊最新文献
Physical model learning based false data injection attack on power system state estimation Federated learning framework for prediction of net energy demand in transactive energy communities Editorial Board Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market Hybrid day-ahead and real-time energy trading of renewable-based multi-microgrids: A stochastic cooperative framework
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